The Detection of Asset Price Bubbles in the Cryptocurrency Markets with an Application to Risk Management and the Measurement of Model Risk

This study presents an analysis of the impact asset price bubbles on markets for cryptocurrencies and con-siders standard risk management measure Value-at-Risk (“VaR”). We apply theory local martingales, present a styled model in continuous time perform simulation experiment featuring one- two-dimensional Stochastic Differential Equation (“SDE”) systems value through Constant Elasticity Variance (“CEV”) process that can detect bubble behavior. In empirical across several widely traded cryptocurrencies, we find estimated parameters one-dimensional SDE do not show evidence However, if estimate system jointly with equity market index, bubble, comparing to non-bubble economies it is shown result materially inflated VaR measures. The implication this finding portfolio rather than acting as diversifying class, may only be highly correlated other assets but have anti-diversification properties inflate downside risks portfolios combining these types. also arising from mispecifying driving by ignoring relationship another representative applying principle relative entropy, where all studied distributions distance between simulated are almost skewed right very heavy-tailed. majority cases “multipliers” range about two five estimates which could applied establish reserve part economic capital calculation cryptocurrencies.